Observation and modelling of fog at Cold Lake, Alberta, Canada

Di Wu (1), Faisal Boudala (1), Wensong Weng (2), Peter Taylor (2), Ismail Gultepe (1), George A. Isaac (2,3)

(1) Cloud Physics and Severe Weather Research Section, Environment and Climate Change Canada, Toronto, Ontario

(2) Department of Earth & Space Science & Engineering,YorkUniversity,Toronto, Canada (pat@yorku.ca),

(3) Weather Impacts Consulting Incorporated, Barrie, Ontario, Canada

Geophysical Research AbstractsVol. 19, EGU2017-10199, 2017EGU General Assembly 2017

York research supported by NSERC Engage grant with AMEC-FW

Google earth map of Cold Lake (54° N, 110° W, 541 m ASL) and ECCC’s ground instruments site

DND METAR station and ECCC’s ground instruments site

Ground instruments at Cold Lake, Alberta

The ground-based surface and remote sensing instruments that include a Vaisala PWD22 present weather sensor, multi-channel microwave profiling radiometer (PMWR), Jenoptik CHM15k ceilometer, and WXT520 weather transmitter are used for. The PWD22 measures visibility, and both precipitation type and intensity. A fog detector (FMD), meteorological particle spectrometer (MPS) , and Sunphotometer have been added on the extended platform. Further analysis of these data will provide the fall velocity, and size distribution and shape of the falling fog hydrometeors.

The classification of fog types,defined as precipitation fog,advection fog, cloud base loweringfog, radiation fog and evaporationfog, is based on the methodologyfollowed by Tardif and Rasmussen(2006) (Table 2 and Figure 4).

Climatological characteristics of fog

The classification of fog (visibility<1km) types, defined as radiation fog (frequency 69%),precipitation fog (17%), cloud base lowering fog (7%), advection fog (5%), and oneunknown fog (2%) in 2015-2016, is based on the methodology followed by Tardif andRasmussen (2007). Note MST = UTC - 7hrs

The frequency of observed visibility, ceiling, surface level temperature, RH, wind speed and direction distribution in 2015-2016

Case study: Oct 24, 2016

20161024 fog case (advection fog)

Vertical profiles measured by MWR and simulated by HRDPS (EC High Resolution Deterministic Prediction

System) at time of fog conditions (7am MDT)

Upstream/initial conditions: a 1-D time dependent model.

Surface cooling, dTs(t)/dt < 0, so qs(Ts) also decreasing. Water surface is a water droplet sink, Ql = 0, and potential source or sink of Qv. Settling velocity (Nakanishi, 2000 proposes VT = 106(Ql/N0)2/3 ms-1 - an empirical equation! with N0 fixed at 108m-3). Typical values, Ql = 10-4, VT = 0.01ms-1.

Heat and Momentum

Water Vapour, Qv

Liquid water droplets, Ql, diffusion + settling, VT.

Long wave and solar radiation absorbtion, emission

Ql = 0

Clouds

Θl , Θv and Qw

potential temperature of dry air with same density as moist air + water droplets - needed for stability considerations and TKE equation.

1-D equations

TKE and closure

260 264 268 272 276Temperature, T (K)

0

400

800

1200H

eigh

t, z

(m)

Time (hr)0123456

260 264 268 272 276Liquid-water potential temperature, Θl (K)

0

400

800

1200

Hei

ght,

z (m

)

Time (hr)0123456

1-D model has 201 grid points, uniform spacing after log-linear z to ςtransform. Top of model at 3 km.

Surface cooled at 2°K per hour for 6 hours, 100% RH at surface

0 1 2 3 4 5Total water content, Qw (g kg-1)

10-2

10-1

100

101

102

103

Hei

ght,

z (m

)

0 0.05 0.1 0.15 0.2 0.25Liquid water content, Ql (g kg-1)

40 60 80 100 120Relative humidity, Qv/Qs

Time (hr)0123456

1 2 3 4 5Total water content, Qw (g kg-1)

0

400

800

1200

Hei

ght,

z (m

)

0 0.05 0.1 0.15 0.2 0.25Liquid water content, Ql (g kg-1)

40 60 80 100 120Relative humidity, Qv/Qs

Time (hr)0123456

Results with Long Wave radiation, More Ql due to radiative cooling.

Linear scale in z

1 2 3 4 5Total water content, Qw (g kg-1)

0

400

800

1200

Hei

ght,

z (m

)

0 0.01 0.02 0.03 0.04 0.05Liquid water content, Ql (g kg-1)

Time (hr)0123456

40 60 80 100 120Relative humidity, Qv/Qs

No LW radiation in model, less fog, later start.

260 264 268 272 276Temperature, T (K)

0

400

800

1200H

eigh

t, z

(m)

Time (hr)0 (NBL)123456

260 264 268 272 276L-w p-temperature, Θl (Κ)

0

400

800

1200

260 264 268 272 276Virtual p-temperature, Θv (Κ)

0

400

800

1200

1 2 3 4 5Total water content, Qw (g kg-1)

0

400

800

1200

0 0.1 0.2 0.3 0.4 0.5TKE (m2s−2)

0

400

800

1200

Hei

ght,

z (m

)

0 2 4 6 8 10Eddy diffusivity, Kh (m2s−1)

0

400

800

1200

-0.08 -0.04 0⟨wθl⟩ (m s−1Κ)

0

400

800

1200

-0.02 -0.01 0 0.01 0.02⟨wqw⟩ (m s−1g kg-1)

0

400

800

1200

220 240 260 280 300 320Downward, upward radiative fluxes (W m-2)

400

800

1200

Hei

ght,

z (m

)

Time (hr)4_Dn5_Dn6_Dn4_Up5_Up6_Up

-20 0 20 40 60Net radiative flux (W m-2)

Time (hrs)4_Net5_Net6_Net

-0.001 -0.0005 0 0.0005 0.001 0.0015 0.002Radiative heating rate (K s-1)

Time (hrs)4_HR5_HR6_HR

Next Model Development Steps (somewhat funding dependent!)

Add solar radiation terms. Resolve issues with lower boundary condition for Ql

– include surface energy budget Get better data on profiles with/without fog. Get the advective case running Use the model to determine combinations of

conditions (upstream/initial profiles, wind speeds, cloud cover, rate of change of surface temperature, etc. etc.) under which fog forms, and dissipates.

Still struggling to clear the fog but slowly making progress in understanding the conditions that allow fog to form. It is not an easy problem!

Slides I do not have time for!

Case study: Surface measurements on a fog day Oct 24, 2016 (Advection fog)

1-D Fog modellingThe condensation of water vapour into droplets and the formation of fog in the Earth's atmospheric boundary layer involves a complex balance between horizontal advection and vertical turbulent mixing of heat and water vapour, cloud microphysical processes involving the numbers and size of available condensation nucleii and radiative transfers of heat, plus the impact of water droplets, and sometimes ice crystals, on visibility. It is a phenomenon which has been studied for many years in a variety of contexts. Over the waters offshore from Newfoundland (Grand Banks) a key factor is the advection of moist air from over warm gulf stream waters to colder Labrador current water - an internal boundary-layer problem. Some basic fog properties and the sensitivity to model parameters, initial and boundary conditions, can be learned from 1-D time dependent (z,t) and 2-D steady state (x-z) models.

Supported by NSERC Engage program and AMEC-Foster Wheeler

Models we have learnt from.

Nakanishi, 2000, Large eddy simuation of radiation fog, Boundary-Layer Meteorology 94: 461–493,

Weng, W, Taylor, P.A. and Salmon J.R., 2010, A 2-D numerical model of boundary-layer flow over single and multiple surface condition changes, J. Wind Eng. Ind. Aerodyn. 98 (2010) 121–132.

Garratt, J.R. and Brost, R.A., 1981, Radiative Effects within and above the Nocturnal Boundary Layer, J.Atmos. Sci, 38, 2730-2746

More closure details

200 220 240 260 280 300 320Downward, upward radiative fluxes (W m-2)

10-2

10-1

100

101

102

103

Hei

ght,

z (m

)

Time (hr)4_Dn5_Dn6_Dn4_Up5_Up6_Up

-20 0 20 40 60 80Net radiative flux (W m-2)

Time (hrs)4_Net5_Net6_Net

-0.001 -0.0005 0 0.0005 0.001 0.0015 0.002Radiative heating rate (K s-1)

Time (hrs)4_HR5_HR6_HR